Triple
T33230841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | caponata |
E850687
|
entity |
| Predicate | canBeServedAtTemperature |
P66977
|
FINISHED |
| Object | warm |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: warm | Statement: [caponata, canBeServedAtTemperature, warm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeServedAtTemperature Context triple: [caponata, canBeServedAtTemperature, warm]
-
A.
recommendedServingTemperature
chosen
Indicates the temperature at which something (typically food or drink) is advised to be served for optimal use or enjoyment.
-
B.
optimalTemperatureCelsius
Indicates the temperature in degrees Celsius at which something operates or performs at its best.
-
C.
requiresCookingTemperature
Indicates that performing the action or preparing the item necessitates reaching or maintaining a specific cooking temperature.
-
D.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
E.
wineServingTemperature
Indicates the temperature at which a particular wine is or should be served.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f349613f988190a1eb75467d167122 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fe78e545888190a239af1a84280fa0 |
completed | May 8, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69fe7842742081908043eb950ed69f92 |
completed | May 8, 2026, 11:56 p.m. |
Created at: May 1, 2026, 1:30 a.m.